Grabbing SPINS gradients
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## Loading required package: ExPosition
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## New names:
## Rows: 164640 Columns: 8
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): ROI, Network, Subject, Site dbl (4): ...1, grad1, grad2, grad3
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`
## [1] "record_id" "scanner"
## [3] "diagnostic_group" "demo_sex"
## [5] "demo_age_study_entry" "scog_rmet_total"
## [7] "scog_er40_total" "scog_tasit1_total"
## [9] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [11] "scog_tasit2_parsar" "scog_tasit3_lie"
## [13] "scog_tasit3_sar" "np_domain_tscore_process_speed"
## [15] "np_domain_tscore_att_vigilance" "np_domain_tscore_work_mem"
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"
## New names:
## Rows: 467 Columns: 43
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (36): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`
grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav$record_id[order(lol_spins_behav$record_id)]
# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]
# complete.cases(spins_grads_wide)
# complete.cases(lol_spins_behav)
kept.sub <- lol_spins_behav$record_id[complete.cases(lol_spins_behav)==TRUE] # 420
## grab the matching data
behav.dat <- lol_spins_behav[kept.sub,c(6:19)]
spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]
## variables to regress out
regout.dat <- var2regout_num[kept.sub,]
behav_all <- lol_spins_behav[kept.sub,]
table_one <- CreateTableOne(vars = colnames(behav_all)[4:19], strata="diagnostic_group",data=behav_all)
lol_demo <-
read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
filter(exclude_MRI==FALSE,
exclude_meanFD==FALSE,
exclude_earlyTerm==FALSE) %>% as.data.frame
## New names:
## Rows: 467 Columns: 46
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (39): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
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## * `` -> `...1`
lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
rownames(lol_demo) <- lol_demo$record_id
lol_demo_match <- lol_demo[kept.sub,]
spins_demo <- lol_demo_match %>%
select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
scog_tasit1_total,
scog_tasit2_sinc,
scog_tasit2_simpsar,
scog_tasit2_parsar,
scog_tasit3_lie,
scog_tasit3_sar,
np_domain_tscore_att_vigilance,
np_domain_tscore_process_speed,
np_domain_tscore_work_mem,
np_domain_tscore_verbal_learning,
np_domain_tscore_visual_learning,
np_domain_tscore_reasoning_ps,
#bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
#fd_mean_rest
) %>% data.frame
colnames(spins_demo)
## [1] "demo_sex" "demo_age_study_entry"
## [3] "diagnostic_group" "scog_rmet_total"
## [5] "scog_er40_total" "scog_tasit1_total"
## [7] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [9] "scog_tasit2_parsar" "scog_tasit3_lie"
## [11] "scog_tasit3_sar" "np_domain_tscore_att_vigilance"
## [13] "np_domain_tscore_process_speed" "np_domain_tscore_work_mem"
## [15] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [17] "np_domain_tscore_reasoning_ps"
rownames(spins_demo) <- lol_demo_match$subject
## Statistic testing
demo_summary <- spins_demo %>%
select(-demo_sex, demo_age_study_entry) %>%
gather(key = variable, value = value, -diagnostic_group) %>%
group_by(diagnostic_group, variable) %>%
summarise(value = list(value)) %>%
spread(diagnostic_group, value) %>%
group_by(variable) %>%
mutate(
mean_ssd = mean(unlist(case), na.rm = TRUE),
mean_control = mean(unlist(control), na.rm = TRUE),
sd_ssd = sd(unlist(case), na.rm = TRUE),
sd_control = sd(unlist(control), na.rm = TRUE),
var_equal_p = var.test(unlist(case), unlist(control))$p.value,
norm_ssd = shapiro.test(unlist(case))$p.value,
norm_control = shapiro.test(unlist(control))$p.value) %>%
mutate(method = if_else(norm_ssd > .05 | norm_control > .05,
"boot", if_else(var_equal_p > .05, "welch", "student"))) %>%
mutate(
t_value = case_when(
method == "boot" ~ 999,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$statistic,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$statistic),
df = case_when(
method == "boot" ~ 999,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$parameter,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$parameter),
p_value = case_when(
method == "boot" ~ boot.t.test(unlist(case), unlist(control))$boot.p.value,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$p.value,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$p.value))
## `summarise()` has grouped output by 'diagnostic_group'. You can override using
## the `.groups` argument.
demo_summary$p_FDR <- p.adjust(demo_summary$p_value, method = "fdr")
demo_summary
## # A tibble: 15 x 15
## # Groups: variable [15]
## variable case control mean_~1 mean_~2 sd_ssd sd_co~3 var_eq~4 norm_ssd
## <chr> <lis> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 demo_age_stud~ <dbl> <dbl> 31.4 31.9 9.77 10.4 3.71e- 1 1.02e- 9
## 2 np_domain_tsc~ <dbl> <dbl> 39.5 47.6 11.7 12.7 2.16e- 1 4.28e- 3
## 3 np_domain_tsc~ <dbl> <dbl> 39.7 53.1 13.2 10.1 2.33e- 4 2.89e- 1
## 4 np_domain_tsc~ <dbl> <dbl> 42.9 48.8 11.0 9.54 5.11e- 2 1.59e- 3
## 5 np_domain_tsc~ <dbl> <dbl> 40.7 50.3 8.94 9.44 4.32e- 1 7.04e- 4
## 6 np_domain_tsc~ <dbl> <dbl> 38.7 48.4 12.5 10.1 2.93e- 3 2.68e- 2
## 7 np_domain_tsc~ <dbl> <dbl> 41.3 49.2 11.2 11.4 8.26e- 1 3.88e- 1
## 8 scog_er40_tot~ <dbl> <dbl> 31.8 33.5 4.55 3.32 1.45e- 5 1.47e-13
## 9 scog_rmet_tot~ <dbl> <dbl> 24.6 27.6 5.26 3.82 1.12e- 5 7.25e- 7
## 10 scog_tasit1_t~ <dbl> <dbl> 22.5 24.6 3.64 2.14 6.71e-13 1.79e-10
## 11 scog_tasit2_p~ <dbl> <dbl> 15.7 18.5 3.95 2.09 0 3.94e-12
## 12 scog_tasit2_s~ <dbl> <dbl> 14.9 18.5 4.94 1.92 0 4.55e-13
## 13 scog_tasit2_s~ <dbl> <dbl> 16.9 17.5 3.19 2.69 1.77e- 2 1.06e-14
## 14 scog_tasit3_l~ <dbl> <dbl> 24.8 27.2 4.12 3.64 8.62e- 2 1.25e- 6
## 15 scog_tasit3_s~ <dbl> <dbl> 23.5 27.5 5.15 3.62 1.44e- 6 1.27e- 6
## # ... with 6 more variables: norm_control <dbl>, method <chr>, t_value <dbl>,
## # df <dbl>, p_value <dbl>, p_FDR <dbl>, and abbreviated variable names
## # 1: mean_ssd, 2: mean_control, 3: sd_control, 4: var_equal_p
lol_original$scanner %>% table
## .
## CMH CMP MRC MRP ZHH ZHP
## 135 30 66 79 42 98
table(regout.dat$demo_sex_num)
##
## 0 1
## 159 261
behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg2plot <- apply(grad.dat, 2, function(x){
model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
return(model$residual + model$coefficient[1])
} )
networks <- read_delim("../networks.txt",
"\t", escape_double = FALSE, trim_ws = TRUE) %>%
select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
distinct() %>%
add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
arrange(NETWORKKEY)
## Rows: 718 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
networks$hex <- darken(networks$hex, 0.2)
# oi <- networks$hex
# swatchplot(
# "-40%" = lighten(oi, 0.4),
# "-20%" = lighten(oi, 0.2),
# " 0%" = oi,
# " 20%" = darken(oi, 0.2),
# " 25%" = darken(oi, 0.25),
# " 30%" = darken(oi, 0.3),
# " 35%" = darken(oi, 0.35),
# off = c(0, 0)
# )
# networks
## match ROIs to networks
ROI.network.match <- cbind(spins_grads$ROI, spins_grads$Network) %>% unique
ROI.idx <- ROI.network.match[,2]
names(ROI.idx) <- ROI.network.match[,1]
### match networks with colors
net.col.idx <- networks$hex
names(net.col.idx) <- networks$NETWORK
## design matrix for subjects
sub.dx <- spins_dx_org[kept.sub,]
diagnostic.dx <- sub.dx$diagnostic_group %>% as.matrix
diagnostic.dx <- recode(diagnostic.dx, !!!c("case" = "SSD"))
diagnostic.col.idx <- c("SSD" = "darkorchid3",
"control" = "gray50")
diagnostic.col <- list()
diagnostic.col$oc <- recode(diagnostic.dx, !!!diagnostic.col.idx) %>% as.matrix()
diagnostic.col$gc <- diagnostic.col.idx %>% as.matrix
## design matrix for columns - behavioral
behav.dx <- matrix(nrow = ncol(behav.dat), ncol = 1, dimnames = list(colnames(behav.dat), "type")) %>% as.data.frame
behav.col <- c("scog" = "#D97614",#"#F28E2B",
"np" = "#3F7538",#"#59A14F",
"bsfs" = "#D37295")
behav.dx$type <- sub("(^[^_]+).*", "\\1", colnames(behav.dat))
behav.dx$type.col <- recode(behav.dx$type, !!!behav.col)
## design matrix for columns - gradient
grad.dx <- matrix(nrow = ncol(grad.dat), ncol = 4, dimnames = list(colnames(grad.dat), c("gradient", "ROI", "network", "network.col"))) %>% as.data.frame
grad.dx$gradient <- sub("(^[^.]+).*", "\\1", colnames(grad.dat))
grad.dx$ROI <- sub("^[^.]+.(*)", "\\1", colnames(grad.dat))
grad.dx$network <- recode(grad.dx$ROI, !!!ROI.idx)
grad.dx$network.col <- recode(grad.dx$network, !!!net.col.idx)
## get different alpha for gradients
grad.col.idx <- c("grad1" = "grey30",
"grad2" = "grey60",
"grad3" = "grey90")
grad.dx$gradient.col <- recode(grad.dx$gradient, !!!grad.col.idx)
## for heatmap
col.heat <- colorRampPalette(c("red", "white", "blue"))(256)
pls.res <- tepPLS(behav.reg, grad.reg, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)
pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000, nf2keep = 5, eig = TRUE)
## Registered S3 method overwritten by 'data4PCCAR':
## method from
## print.str_colorsOfMusic PTCA4CATA
pls.boot$bootRatiosSignificant.j[abs(pls.boot$bootRatios.j) < 2.88] <- FALSE
pls.boot$bootRatiosSignificant.i[abs(pls.boot$bootRatios.i) < 2.88] <- FALSE
pls.inf <- data4PCCAR::perm4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000)
# ## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1
## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs,
p.ev = pls.inf$pEigenvalues)
## Print singular values
pls.res$TExPosition.Data$pdq$Dv
## [1] 7.1330565 2.1157103 1.9079739 1.6310932 1.5321817 1.4132437 1.2515730
## [8] 1.1846931 1.1177672 1.0190911 0.9319565 0.9106204 0.8248812 0.7818378
## Print eigenvalues
pls.res$TExPosition.Data$eigs
## [1] 50.8804950 4.4762303 3.6403644 2.6604652 2.3475808 1.9972578
## [7] 1.5664349 1.4034979 1.2494036 1.0385467 0.8685430 0.8292296
## [13] 0.6804289 0.6112703
pls.res$TExPosition.Data$t
## [1] 68.5261515 6.0286134 4.9028643 3.5831302 3.1617357 2.6899186
## [7] 2.1096838 1.8902392 1.6827041 1.3987209 1.1697588 1.1168113
## [13] 0.9164057 0.8232624
## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)
## [1] 81.59259
sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))
## [1] 0.004955818
Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.
lxly.out[[1]]
lx1.ssd <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "case"), 1]
lx1.hc <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "control"), 1]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
## Warning: Vectorized input to `element_text()` is not officially supported.
## i Results may be unexpected or may change in future versions of ggplot2.
cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)
## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
\[CV_{control} = \] 1.04 % bootstrap CI: [0.81, 1.35]
\[CV_{SSD} = \] 2.34 % bootstrap CI: [1.83, 3.12]
lxly.out[[2]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))
cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)
lxly.out[[3]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))
cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)
lxly.out[[4]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))
cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)
## merging atlas and data by 'label'
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